Predicting full-reference video quality measures using HEVC bitstream-based no-reference features

Shahid, Muhammad; Panasiuk, Joanna; Wallendael, Glenn Van; Barkowsky, Marcus; Lövström, Benny

doi:10.1109/qomex.2015.7148118

Cited by 22 publications

(18 citation statements)

References 6 publications

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“…Based on the reported finding of our work, more experiments can be performed to learn on how to optimize the functionality of existing objective quality metrics. Moreover, future works should also pay attention to consider test stimuli encoded with the new video coding standard HEVC for which different approaches of objective quality estimation have already started to be published [23].…”

Section: Resultsmentioning

confidence: 99%

Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming

Sogaard¹,

Krasula²,

Shahid³

et al. 2016

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Objective video quality metrics are designed to estimate the quality of experience of the end user. However, these objective metrics are usually validated with video streams degraded under common distortion types. In the presented work, we analyze the performance of published and known full-reference and noreference quality metrics in estimating the perceived quality of adaptive bit-rate video streams knowingly out of scope. Experimental results indicate not surprisingly that state of the art objective quality metrics overlook the perceived degradations in the adaptive video streams and perform poorly in estimating the subjective quality results.

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Section: Resultsmentioning

confidence: 99%

Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming

Sogaard¹,

Krasula²,

Shahid³

et al. 2016

Self Cite

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“…Promising examples of cognitive approaches are adaboost for assessing artifact levels in videos [15], the bitstream based artificial neural network [16], the artificial neural network for jerkiness evaluation [17], and the regression framework for estimating the objective quality index [18].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Quality Assessment in Live Video Streaming

Vega

Mocanu

Famaey

et al. 2017

IEEE Signal Process. Lett.

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Abstract-Video content providers put stringent requirements on the quality assessment methods realized on their services. They need to be accurate, real-time, adaptable to new content, and scalable as the video set grows. In this letter, we introduce a novel automated and computationally efficient video assessment method. It enables accurate real-time (online) analysis of delivered quality in an adaptable and scalable manner. Offline deep unsupervised learning processes are employed at the server side and inexpensive no-reference measurements at the client side. This provides both real-time assessment, as well as performance comparable to the full reference counter-part, while maintaining its no-reference characteristics. We tested our approach on the LIMP Video Quality Database (an extensive packet loss impaired video-set) obtaining a correlation between 78% and 91% to the FR benchmark (the Video Quality Metric, VQM). Due to its unsupervised learning essence, our method is flexible, dynamically adaptable to new content and scalable with the number of videos.Index Terms-Deep learning, unsupervised learning, video quality assessment, multimedia video services.

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“…Promising examples of cognitive approaches are the Adaboost approach for assessing artifacts levels in videos, by Vink and de Haan [43]; the bitstream based artificial neural network, by Shahid et al [35]; the artificial neural network for jerkiness evaluation, by Xue et al [46]; and the regression framework for estimating the objective quality index (SSIM or PSNR), by Shanableh [36]. However, these approaches are usually based on supervised learning techniques, thus requiring labelled data to perform the offline training.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised deep learning for real-time assessment of video streaming services

Vega

Mocanu

Liotta

2017

Multimed Tools Appl

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Evaluating quality of experience in video streaming services requires a quality metric that works in real time and for a broad range of video types and network conditions. This means that, subjective video quality assessment studies, or complex objective video quality assessment metrics, which would be best suited from the accuracy perspective, cannot be used for this tasks (due to their high requirements in terms of time and complexity, in addition to their lack of scalability). In this paper we propose a light-weight No Reference (NR) method that, by means of unsupervised machine learning techniques and measurements on the client side is able to assess quality in real-time, accurately and in an adaptable and scalable manner. Our method makes use of the excellent density estimation capabilities of the unsupervised deep learning techniques, the restricted Boltzmann machines, and light-weight video features computed just on the impaired video to provide a delta of quality degradation. We have tested our approach in two network impaired video sets, the LIMP and the ReTRiEVED video quality databases, benchmarking the results of our method against the well-known full reference metric VQM. We have obtained levels of accuracy of at least 85% in both datasets using all possible cases.

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Predicting full-reference video quality measures using HEVC bitstream-based no-reference features

Cited by 22 publications

References 6 publications

Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming

Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming

Deep Learning for Quality Assessment in Live Video Streaming

Unsupervised deep learning for real-time assessment of video streaming services

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